You would have wanted to automate the AI without having to take the help from the data scientists. But things are not that simple even if the technology exists in the machine learning. You still need human experts for data exploration and to build the neural network machine learning solutions. The tasks are handled by data scientist who explore the data and also to make accurate predictions. If they get the right tools they can perform their jobs well. The skills these professional possess can’t be automated and if you are seeking to buy automated AI, you should know what can be automated and what can’t.
Automating Machine Learning: What can be achieved?
The data scientist tune algorithm and they will have to work on many combinations to find out what works the best. As the data scientists are performing a hyper parameter search they test their various combinations. A hyper parameter search can be automated and there are solutions for this. You can search for the best combination of hyper parameters with different search algorithms e.g. grid search, random search and Bayesian methods.
One thing that the AI vendors do is run the same data through many algorithms. It helps in determining which algorithm can learn the best on your data. But there are limitations of range of algorithms that can be picked and how well they are being tuned. As there are only certain domains of inputs, a few tools can select the most relevant features from the domain. It will not solve the larger problem of identifying the right features and then gathering them.
So AutoML represents a shift in the way organizations of all sizes approach the machine learning and the data science. When you use it your reliance on the professionals is reduced significantly. It becomes rather easy to build and use the machine learning models in the real world. They run a systematic process on the raw data and select the models that pull the information on the raw data. It is also called as signal in the noise. The automated machine learning incorporates the best practices of the machine learning from the leading data scientists and makes it more accessible to the organization.
Importance of Automated Machine Learning
When you construct a machine learning model, it is a multi-step process which requires some domain knowledge, expertise of mathematics and the skills of computer science. Then there are many ways an error can creep in. It can make the model inaccurate and also devalues the insights you might get from the model. With the help form the automated machine learning, the organizations can use the baked-in knowledge of the data scientists without needing to develop the capabilities on the own.
When you have automated neural network machine learning solution, it makes the things a lot easy for the organization as you may not have to rely on the experts fully.